Method and apparatus for detecting the presence of a signal in a frequency band using non-uniform sampling

ABSTRACT

A method and apparatus for detecting the presence of a signal in a frequency band using non-uniform sampling includes an analog to digital converter (ADC) ( 110 ) for sampling an analog input signal ( 105 ) to create discrete signal samples ( 115 ), an ADC exciter ( 120 ) for exciting the ADC to sample at non-uniform time periods, a digital filter ( 130 ) for converting the discrete signal samples into an energy versus frequency spectrum ( 300 ), and an energy comparator ( 140 ) coupled to an output of the digital filter. The energy comparator ( 140 ) detects the presence of any frequency bands exceeding an energy setpoint.

CROSS REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 11/609,980 filed Dec. 13, 2006 by Ajay K. Luthra and entitled“Method and Apparatus for Detecting the Presence of a Signal in aFrequency Band Using Non-Uniform Sampling.” This related application ishereby incorporated by reference herein in its entirety, and prioritythereto for common subject matter is hereby claimed.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to signal processing. Moreparticularly, the disclosure relates to a technique using non-uniformsampling for detecting the presence of a signal in a frequency band.

BACKGROUND

In order to detect the presence of a signal in a frequency band, usuallyan input signal is uniformly sampled in the time domain and converted tothe frequency domain. Then the frequency domain spectrum is analyzed forenergy peaks indicating the possible presence of a signal. One problemwith such an approach is that, at high frequencies, a large number ofsamples need to be taken in order to overcome aliasing. Such a largenumber of samples require a large amount of processing and power whichtends to stress the receiver.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, and which together with the detailed description below areincorporated in and form part of the specification, serve to furtherillustrate various embodiments and to explain various principles andadvantages all in accordance with the present disclosure.

FIG. 1 shows a block diagram of a mobile device incorporating a signaldetector using non-uniform sampling in accordance with an embodiment.

FIG. 2 shows a process flow diagram for a signal detector in accordancewith an embodiment.

FIG. 3 shows an example energy versus frequency spectrum of the discretesignal samples in accordance with an embodiment.

FIG. 4 shows an example of non-uniform sampling in accordance with anembodiment.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

DETAILED DESCRIPTION

Before describing in detail embodiments that are in accordance with thepresent disclosure, it should be observed that the embodiments resideprimarily in searching a signal in a large frequency band. It includesreceiving an analog signal, non-uniformly sampling the analog signal,converting the input signal to frequency domain, and analyzing thefrequency domain spectrum to detect a potential presence of a desiredsignal in the frequency band. Accordingly, the apparatus and methodcomponents have been represented where appropriate by conventionalsymbols in the drawings, showing only those specific details that arepertinent to understanding the embodiments of the present invention soas not to obscure the disclosure with details that will be readilyapparent to those of ordinary skill in the art having the benefit of thedescription herein.

FIG. 1 shows a block diagram of a mobile device incorporating a signaldetector 100. The mobile device includes a user interface 114 forreceiving user input such as voice or data from a microphone or keypad,a processor 150, a transmitter 108 for transmitting a signal out througha duplexer 104 and an antenna 102, a receiver 106 for receiving a signalthrough the antenna 102 and the duplexer 104, and a memory 112. The userinterface 114, for example, can include a microphone, an ear piece, adisplay, a keyboard, and so on. The antenna 102 receives and transmitssignals. The memory 112 is used for storage. The duplexer 104 isresponsible for simultaneous transmission and reception of signals withthe assistance of the transmitter 108 for transmitting and the receiver106 for receiving. The receiver 106 includes the signal detector 100.The processor 150 and the memory 112 are connected to the signaldetector 100.

The signal detector 100 has an analog to digital converter (ADC) 110, anADC exciter 120, a digital filter 130, an energy comparator 140, and iscoupled to the processor 150. When operational, the signal detector 100detects the presence of a signal in a frequency band. It helps to reducethe amount of processing in a wireless communication device (e.g., themobile device shown in FIG. 1) in a communication system while avoidingaliasing. The signal detector can use a slower average sampling rate anda significantly smaller number of samples for detecting the potentialpresence of a desired signal in a frequency band. This is done bysampling the analog input signal non-uniformly to produce non-uniformdiscrete signal samples.

Non-uniform discrete signal samples are produced by taking samples of ananalog input signal 105 at discrete time instants such that the timespacing between any pair of consecutive sampling times may be differentfrom any other pair. Thus, the sampling times are not necessarilyequidistant from each other in time. FIG. 4 shows an example ofnon-uniform sampling. The discrete signal samples are taken at timeinstants 0, A, B, C, D, E, and so on. The time spaces OA, AB, BC, CD,DE, etc. are dissimilar in this example of non-uniform sampling.Non-uniform sampling allows a user to avoid the Nyquist criteria inorder to prevent aliasing. Hence, the average sampling rate and thetotal number of samples required to detect the presence of a signal in afrequency band can be significantly reduced.

Returning to FIG. 1, the ADC 110 receives the analog input signal 105and converts it to discrete signal samples 115 taken using non-uniformsampling time periods. The ADC 110 includes a switch that closes atnon-uniform time periods determined by the ADC exciter 120, so that theamplitude of the analog input signal is passed through the switch atthose non-uniform time periods and otherwise it gets blocked at theswitch. This leads to the production of discrete signal samples atnon-uniform time periods.

The ADC exciter 120 determines the non-uniform sampling periods at whichthe ADC 110 samples the analog input signal 105. The memory 112 providesthe ADC exciter 120 with a total number of samples N 126 to be taken.The total number of samples N 126 determines the average side lobeenergy level when the samples are converted to a frequency domain. Theformula −10log(N) approximates the average suppression of the side lobelevels relative to the main lobe. For example, if the total number ofsamples N is 1000, then −10log(N) results in an average −30 dBsuppression of the side lobe level. If, theoretically, only −20 dBsuppression of the average side lobe level is required to detect mainlobes of interest, then the total number of samples N would be set to100 by the processor 150. The whole duration of the signal, the totalnumber of samples N divided by the average sampling rate R, determinesthe lobe widths in the frequency domain. For example, if the sampleswere taken over 1 micro sec, the width of each lobe will beapproximately 1 MHz. The average sampling rate could either bepredetermined or can be adapted while acquiring the signal. The ADCexciter 120 includes a slope detector 122, a pseudo random generator124, or a combination of both.

In versions of the ADC exciter 120 with a slope detector 122, thenon-uniform sampling times are decided depending upon the slope of theanalog input signal 105. In this example, the localized sample spacingis inversely proportional to the currently-detected slope as will beexplained with reference to FIG. 4. In this simplified embodiment, thelocal sampling rate is increased if the slope is steeper and isdecreased if the slope is flatter. The slope can increase positively aswell as negatively. In FIG. 4 segment 420, the slope is flat and onlyone sample is taken at C. In segment 410, the slope is steeperpositively and two samples are taken at time instants A and B. Insegment 430, the slope is steeper negatively and four samples are takenat time instants D, E, F, and G.

Instead of a slope detector 122, a pseudo random generator 124 can beused to determine the spacing between the discrete signal samples to betaken. Pseudo random generators 124 are well known, and the outputsequence determines the non-uniform sampling time periods. The pseudorandom generator 124 can be adjusted so that the average sampling rate Ris generally maintained over the duration of the analog input signal 105as a whole.

Another variation can be the combination of the slope detector 122 andthe pseudo random generator 124. In this combination, the slope detector122 provides an excitation to the pseudo random generator 124 such thatthe sampling rate is locally increased within the pseudo randomgenerator 124 when the slope is steeper and the sampling rate is locallydecreased when the slope is flatter but the average sampling rate R isgenerally maintained over the duration of the signal as a whole.

The digital filter 130 converts the discrete signal samples 115generated by the ADC 110 into an energy versus frequency spectrum 135 inthe frequency domain. This is done by taking Fourier transforms of thediscrete signal samples. The energy versus frequency spectrum producedby the digital filter 130 can be similar to the simplified one shown inFIG. 3.

The energy comparator 140 detects the presence of any frequency bandsexceeding an energy setpoint. The energy setpoint is determined ormodified by the processor 150 and provided to the energy comparator 140.The energy setpoint can be determined in a number of ways.

In a first method, the processor 150 determines or modifies the energysetpoint empirically. Such a setpoint, which is fixed at a particularenergy level depending on historical or test information, is shown asenergy setpoint 360 in the example in FIG. 3. This setpoint 360 happensto lie below two lobes 320 and 330. Depending on the distribution ofother energy versus frequency spectra, the setpoint 360 may lie belowgreater than two lobes or fewer than two lobes. In a second method, theprocessor 150 determines the energy setpoint such that it lies belowsome specified number of frequency bands in the energy versus frequencyspectrum 135. In the example in FIG. 3, the second type of energysetpoint, setpoint 350, lies below the three highest frequency bands ofthe energy versus frequency spectrum 135. In other words, the threelobes 310, 320, 330 with the highest energy levels are selected ashaving frequency bands of interest. For this second method, three lobesare always selected, regardless of the distribution of other energyversus frequency spectra. In a third method, the processor 150 candetermine the energy setpoint such that it is a percentage of the lobe330 with the highest energy level in the energy versus frequencyspectrum 135. In the example in FIG. 3, setpoint 370 is the third typeof energy setpoint. Here in FIG. 3, the energy setpoint is determinedsuch that it is ninety percent of the highest energy level. In thisexample only the lobe 330 is selected. In a fourth method, the processorcan take the average side lobe height and multiply it by a factorgreater than 1 (e.g., 10) to calculate the setpoint. All the lobeshigher than the setpoint will be considered as lobes of interest. As avariant of the empirical determination method, the energy setpoint canbe a predetermined value that is stored in the energy comparator 140 ormemory 112, and in such a case the processor 150 will not be required tomodify or select the setpoint.

The processor 150 shown in FIG. 1 can be used for performing otherfunctions such as varying the total number of samples N over the wholesignal as well as the average sampling rate R and providing feedback tothe ADC exciter 120. The variation of the sampling time periods is basedon the fact that, when non-uniformly sampling, the Fourier transformbehaves differently compared to uniform sampling. In non-uniformsampling, the side lobes do not die down as in uniform sampling. Insteadnon-uniform sampling spreads the energy of the aliased lobes into theside lobes. Therefore, there is only one main lobe and no high aliasedbands in the Fourier transform of non-uniform digital signal samples. Asenergy is spread into the side bands, the energy level of the side lobesgoes up. Therefore it becomes relatively harder to detect a weak signalin the presence of strong signals. On average, the side lobes are at −10log (N) dB lower than the main lobe, where N is the total number ofsamples. As a result, the total number of samples N should be decidedsuch that the side lobe energy level in the frequency domain will be lowenough to accommodate the expected dynamic range of the signals.

As stated previously, the energy comparator 140 detects the presence ofany frequency bands exceeding an energy setpoint. This is done bycomparing the energy of a frequency band from the energy versusfrequency spectrum 135 with the energy setpoint that is determined ormodified by the processor 150. If the energy of the band is foundgreater than the energy setpoint, the presence of the signal isindicated; otherwise the mobile device has found no frequency band ofinterest as previously described with reference to FIG. 3.

The energy comparator 140 optionally includes an accumulator 145. Thework of the accumulator 145 starts if the energy comparator 140 fails toidentify an adequate number of lobes of interest or identifies too manylobes of interest. In that case ‘m’ additional sets of N samples can betaken, where ‘m’ is a whole number greater than 1. In this case, thedetection can be made in different ways. One method is by accumulatingthe energy versus frequency spectrum of each of the ‘m’ sets. Thisimplies taking the Fourier transforms of the discrete signal samples ineach one of the ‘m’ sets and overlapping them with each other to producethe final energy versus frequency spectrum for analysis by the energycomparator 140. Another way of detecting in this case is by usingcombinational logic. If a signal is present in more than ‘k’ out of the‘m’ sets of energy versus frequency spectra then the signal isconsidered to be present at a particular frequency band, otherwise it isconsidered not present. Finally, the ‘m×N’ samples can all be digitallyfiltered to effectively create a frequency spectrum from a larger Nvalue.

The output of the energy comparator is a list of frequencies where asignal of interest potentially exists. This list is further taken up andscanned by the receiver 106 in accordance with known scanningtechniques, such as various foreground scanning techniques for signalsin the bands of interest.

FIG. 2 shows a process flow diagram 200 for a signal detector such asthe signal detector 110 shown in FIG. 1. The signal detector receives210 an analog input signal. In the example shown in FIG. 1, the ADC 110within the signal detector 110 receives the analog input signal 105.

The signal detector determines 220 the discrete sampling time instantsat which the samples of the analog input signal are to be taken. In FIG.1, an ADC exciter 120 performs this function. The discrete sampling timeinstants are the non-uniform sampling time instants at which the analoginput signal will be sampled.

The signal detector samples 230 the analog input signal non-uniformly toproduce discrete signal samples. In the example shown in FIG. 1, the ADC110 samples the analog input signal.

The signal detector converts 240 the discrete signal samples to afrequency domain to produce an energy versus frequency spectrum. In theexample shown in FIG. 1, the function is performed by a digital filter130. An example of an energy versus frequency spectrum is explained indetail with reference to FIG. 3.

Returning back to FIG. 2, the signal detector determines 250 if theenergy of a frequency band from the energy versus frequency spectrum isgreater than an energy setpoint. If so, it concludes 260 the potentialpresence of a signal of interest at that frequency band. Otherwise, theabsence of any signal of interest at that frequency band is concluded270. In the example shown in FIG. 1, the energy comparator 140determines 250 and concludes 260, 270 if there potentially is a signalof interest at a particular frequency band. Frequency bands havingpotential signals of interest are passed to the receiver for furtherprocessing. For example, a mobile device may first start foregroundscanning using a mode (e.g., GSM 900) that includes the interestingfrequency bands and only perform foreground scanning In other modes(e.g., GSM 1900) if the first mode is unsuccessful.

FIG. 3 shows an example of an energy versus frequency spectrum 300 ofdiscrete signal samples. This energy versus frequency spectrum 300 issimilar to the energy versus frequency spectrum 135 shown in FIG. 1. Thehorizontal axis represents the different frequency bands and thevertical axis represents the energy of the signal at the correspondingfrequency band of the horizontal axis. The energy versus frequencyspectrum forms lobes 310, 320, 330, 340, 390 and others, whichrepresents a frequency band and its corresponding energy value. Asdescribed with reference to FIG. 1, the discrete signal samples of theanalog input signal produced by the ADC 110 are converted into an energyversus frequency spectrum using a digital filter 130. The lobes 340 andall the other lobes that lie approximately at the same level may beconsidered at a side lobe level. Lobes 310, 320, 330 that lie above theside lobe level may be selected as frequency bands of interest,depending on the energy setpoint 350, 360, 370. The horizontal dashedlines show examples of different energy setpoints 350, 360, 370 thatassist in determining frequency bands of interest. The energy setpointsare determined or modified, for example, by the processor 150 shown inFIG. 1 in a number of ways.

According to the first method a processor determines or modifies anenergy setpoint empirically. Such a setpoint is shown as setpoint 360 inthe example in FIG. 3. With reference to the example of FIG. 3, thereare two frequency bands that have lobes with 320, 330 energy levels thatlie above the setpoint 360. As a result, the frequency bands of lobes320, 330 are considered of interest. Note that being a frequency band ofinterest does not necessarily mean that the mobile device will be ableto acquire a signal in that band. Thus, known techniques may be used toscan these bands of interest to determine if a signal exists (or if thehigh energy is being caused by noise) and if the signal can be acquired(or the system emitting the signal is incompatible with the mobiledevice).

In a second method, the processor determines the energy setpoint suchthat it lies below some specified number of frequency bands in theenergy versus frequency spectrum. In the example in FIG. 3, setpoint 350is a second type of energy setpoint. It is determined such that it liesbelow the three highest frequency bands of the energy versus frequencyspectrum. In other words, the three lobes with the highest energy valuesare selected as having frequency bands of interest. In this example,lobes 330, 320, 310 are selected. Note that in this example, lobe 310 isconsidered a frequency band of interest while other energy setpoints maycause lobe 310 to be considered a side lobe.

A third method determines the energy setpoint such that it is apercentage of the highest energy level in the energy versus frequencyspectrum. In the example in FIG. 3, dashed line is this type of theenergy setpoint 370. Here in FIG. 3, the energy setpoint 370 isdetermined such that it is ninety percent of the highest energy level(shown in lobe 330). Thus, according to the third method, only one lobe330 is selected in this example spectrum 300.

FIG. 4 shows an example 400 of non-uniform sampling of a signal.Non-uniform discrete signal samples are produced by taking samples of ananalog signal at discrete time instants such that the time spacingbetween any pair of consecutive sampling times may be different from anyother pair. The sampling times are not necessarily equidistant from eachother in time. The discrete signal samples in FIG. 4 are taken at timeinstants 0, A, B, C, D, E, and so on. The time spaces OA, AB, BC, CD,DE, etc. are dissimilar in this example of non-uniform sampling. Thesenon-uniform sampling time periods are determined using a slope detectorand/or a pseudo random generator as described with reference to the ADCexciter 120 shown in FIG. 1.

A slope detector such as 122 determines the non-uniform sampling timeinstants depending upon the slope of the analog input signal. In theexample of FIG. 4, a segment 410 of the analog input signal has apositively steep slope and the number of samples taken is two (A, B).The segment 430 of the analog input signal has a negatively steep slopeand the number of samples taken is four (D, E, F, G). On the other hand,segment 420 has a flat slope and only one sample is taken (C).

In FIG. 4, N samples are taken using the pseudo random generator 124and/or the slope detector 122 by locally varying the average samplingrate R. For example, let N be 100 samples. After 100 samples are takenand filtered, the processor 150 checks whether the actual side lobesuppression is at the desired level calculated using the formula−10log(N). In cases when the actual side lobe suppression is meets orexceeds the theoretical side lobe suppression, the frequency bands ofinterest are detected. Otherwise, the signal detector 100 continuestaking sets of N=100 samples until the frequency spectrum has thedesired side lobe suppression.

In other words, if the 100 samples do not map to the desired side lobesuppression due to factors such as high noise, low instantaneous desiredsignal power, peak side lobe level being higher than the maximum desiredvalue, etc. causing the deviation of the side lobe energy levels to begreat enough to be unreliable, then the processor 150 directs the ADCexciter 120 to take another 100 samples. If 200 samples do not result inthe desired side lobe suppression, the processor 150 directs the ADCexciter 120 to take 100 more samples and so on. Therefore, a user canset the relative lobe heights based on the expected dynamic range of thesignal. Yet there is flexibility to accommodate instantaneously changingsignal energy levels.

For the example in FIG. 4, each of m=7 sets contains N=100 samples toachieve adequate side lobe suppression, of which only 3 samples areshown in the figure for keeping the figure clearer and easier tounderstand. Since the first set 461 of N=100 samples was not sufficientto obtain an actual side lobe suppression of −20 dB, the processor 150directed the ADC exciter 120 to take a second set 463 of N=100 moresamples. The processor 150 then directs the ADC exciter 120 to continuetaking sets of N=100 samples until the desired side lobe suppression wasreached. For the example in FIG. 4, the signal detector 100 took sevensets 461, 463, 465, 467, 469, 471, 473 of N=100 samples before reachingthe desired side lobe suppression.(As stated previously, only threesamples from each set of N=100 samples is shown in the FIG. 4 to enhanceclarity). The time duration of each of the ‘m’ sets is not necessarilyequal but each of the ‘m’ sets carries an equal number of samples.

In the example of FIG. 4:

-   -   N total number of samples in each set    -   T seconds over which the analog input signal is captured    -   m number of sets taken to reach the desired side lobe        suppression.

A first set 461 of m sets (m=7) shows samples A, B, C and actuallycontains N=100 samples. A second set 463 of m sets (m=7) shows samplesD, E, F and actually contains N=100 samples. A third set 465 of m sets(m=7) shows samples G, H, I and actually contains N=100 samples. Afourth set 467 of m sets (m=7) shows samples J, K, L and actuallycontains N=100 samples. A fifth set 469 of m sets (m=7) shows samples M,N, P and actually contains N=100 samples. A sixth set 471 of m sets(m=7) shows samples Q, R, S and actually contains N=100 samples. Aseventh set 473 of m sets (m=7) shows samples U, V, W and actuallycontains N=100 samples. Note that each group of three samples as shownin a set implies an N=100 group of samples. Also note that values for mand N are provided as an example to make the invention clearer andeasier for the reader to understand and may be varied significantlydepending on design constraints for the signal detector 100.

As an alternative to taking additional sets of a fixed number N ofsamples, the processor 150 may vary the number N in accordance withvarious algorithms (e.g., N increases in accordance with a presetpattern: 100, 200, 500, 1000, N increases in accordance with a formulaetc.).

In the embodiments shown, the input signal is analog in nature and issampled at non-uniform time periods to produce discrete signal samples.Another variation can be receiving a digital input signal where thesamples are uniformly spaced. These uniformly spaced samples in thedigital signal satisfy the Nyquist criteria. In accordance with thepresent invention, the digital input signal is down-samplednon-uniformly to create a digital signal with non-uniform sampling timeperiods, and the resulting signal is processed like the discrete signalsamples 115 in accordance with FIG. 1 to achieve the same purpose ofdecreasing the amount of processing required to find a frequency band ofinterest.

Thus, the usage of non-uniform sampling for detecting the presence ofsignal in a frequency band helps avoid aliasing. Non-uniform samplingalso allows a reduction in the average sampling rate and uses smallernumber of samples for detection of frequency bands of interest. Thiscontributes to detecting frequency bands of interest using lessprocessing power and can very quickly determine frequency bands ofinterest over a large range of frequencies.

In this document, relational terms such as “first” and “second,” “top”and “bottom,” and the like may be used solely to distinguish one entityor action from another entity or action without necessarily requiring orimplying any actual such relationship or order between such entities oractions. The terms “comprises,” “comprising,” “includes,” “has,” or anyother variation thereof, are intended to cover a non-exclusiveinclusion, such that a process, method, article, or apparatus that“comprises” a list of elements does not include only those elements butmay include other elements not expressly listed or inherent to suchprocess, method, article, or apparatus. An element proceeded by“comprises . . . a” or “includes . . . a” or “has . . . a” does not,without more constraints, preclude the existence of additional identicalelements in the process, method, article, or apparatus that comprisesthe element.

I claim:
 1. A method for detecting a presence of a signal comprising of:receiving an analog input signal; sampling the analog input signal atnon-uniform time periods to produce discrete signal samples acquired atnon-uniform time periods, wherein sampling includes detecting a slope ofthe analog input signal and changing a local sampling rate based on theslope of the analog input signal; transforming the discrete signalsamples to a frequency domain to produce an energy versus frequencyspectrum of the discrete signal samples; and determining that a signalis present in a frequency band when an energy versus frequency spectrumof the discrete signal samples exceeds an energy setpoint at thefrequency band.
 2. The method according to claim 1, wherein the localsampling rate is increased if the slope is steeper.
 3. The methodaccording to claim 2, wherein the local sampling rate is increased ifthe slope is steeper positively.
 4. The method according to claim 2,wherein the local sampling rate is increased if the slope is steepernegatively.
 5. The method according to claim 1, wherein the localsampling rate is decreased if the slope is flatter.
 6. A method fordetecting a presence of a signal comprising of: receiving an analoginput signal; sampling the analog input signal at non-uniform timeperiods to produce discrete signal samples acquired at non-uniform timeperiods, wherein the sampling comprises generating a pseudo randomsequence that determines the non-uniform time periods; transforming thediscrete signal samples to a frequency domain to produce an energyversus frequency spectrum of the discrete signal samples; anddetermining that a signal is present in a frequency band when an energyversus frequency spectrum of the discrete signal samples exceeds anenergy setpoint at the frequency band.
 7. The method of claim 6, whereinthe determining comprises: converting the discrete signal samples intothe energy versus frequency spectrum using digital spectral filtering.8. The method of claim 7, wherein converting comprises: taking Fouriertransforms of the discrete signal samples.
 9. The method of claim 6,wherein determining comprises: setting the energy setpoint.
 10. Themethod of claim 9, wherein setting comprises: multiplying an averageside lobe height by a factor greater than one, wherein the product ofthe multiplication provides the energy setpoint.
 11. The method of claim9, wherein setting comprises: empirically determining a value for theenergy setpoint.
 12. A method for detecting a presence of a signalcomprising of: receiving an analog input signal; taking discrete signalsamples of the analog input signal, wherein the discrete signal samplesare acquired at non-uniform time periods; transforming the discretesignal samples to a frequency domain to produce an energy versusfrequency spectrum of the discrete signal samples; selecting an energysetpoint that lies below a specified number of frequency bands of theenergy versus frequency spectrum; setting the energy setpoint; anddetermining that a signal is present in a frequency band when an energyversus frequency spectrum of the discrete signal samples exceeds anenergy setpoint at the frequency band.
 13. A method for detecting apresence of a signal comprising of: receiving an analog input signal;taking discrete signal samples of the analog input signal, wherein thediscrete signal samples are acquired at non-uniform time periods,wherein more discrete signal samples are taken at the non-uniform timeperiods if a side lobe energy level is greater than expected;transforming the discrete signal samples to a frequency domain toproduce an energy versus frequency spectrum of the discrete signalsamples; and determining that a signal is present in a frequency bandwhen an energy versus frequency spectrum of the discrete signal samplesexceeds an energy setpoint at the frequency band.